Update app.py

app.py

@@ -5,21 +5,14 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
 model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
 
-def generate_summary(text):
-    inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
+def generate_summary(text, prompt):
+    # Combine text and prompt into one input
+    combined_input = f"{prompt}: {text}"
+    inputs = tokenizer([combined_input], max_length=1024, return_tensors='pt', truncation=True)
     summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     
-    # Post-process the summary to include only specific points
-    important_points = extract_important_points(summary)
-    
-    return important_points
-
-def extract_important_points(summary):
-    # Filter the summary for sentences containing keywords related to change requests or important points
-    keywords = ["change", "request", "important", "needs", "must", "critical", "required", "suggested"]
-    filtered_lines = [line for line in summary.split('. ') if any(keyword in line.lower() for keyword in keywords)]
-    return '. '.join(filtered_lines)
+    return summary
 
 # Initialize session state for input history if it doesn't exist
 if 'input_history' not in st.session_state:
@@ -28,29 +21,31 @@ if 'input_history' not in st.session_state:
 # Streamlit interface
 st.title("Text Summarization App")
 
-# User text input
-user_input = st.text_area("Enter the text you want to summarize", height=200)
+# User text inputs
+bulk_text = st.text_area("Enter the bulk text you want to summarize", height=200)
+prompt = st.text_input("Enter the prompt for the summary", "What are the key points?")
 
 if st.button("Generate Summary"):
-    if user_input:
+    if bulk_text and prompt:
         with st.spinner("Generating summary..."):
-            summary = generate_summary(user_input)
+            summary = generate_summary(bulk_text, prompt)
             
         # Save the input and summary to the session state history
-        st.session_state['input_history'].append({"input": user_input, "summary": summary})
+        st.session_state['input_history'].append({"text": bulk_text, "prompt": prompt, "summary": summary})
         
-        st.subheader("Filtered Summary:")
+        st.subheader("Generated Summary:")
         st.write(summary)
     else:
-        st.warning("Please enter text to summarize.")
+        st.warning("Please enter both the bulk text and the prompt.")
 
 # Display the history of inputs and summaries
 if st.session_state['input_history']:
     st.subheader("History")
     for i, entry in enumerate(st.session_state['input_history']):
-        st.write(f"**Input {i+1}:** {entry['input']}")
+        st.write(f"**Input {i+1} (Text):** {entry['text']}")
+        st.write(f"**Prompt {i+1}:** {entry['prompt']}")
         st.write(f"**Summary {i+1}:** {entry['summary']}")
         st.write("---")
 
 # Instructions for using the app
-st.write("Enter your text in the box above and click 'Generate Summary' to get a summarized version of your text.")
+st.write("Enter your bulk text and a prompt for summarization, then click 'Generate Summary' to get a summarized version based on your prompt.")